class_docstr: Randomly apply some photometric transforms to an image.
  This was used in PIRL - https://arxiv.org/abs/1912.01991
  
  The photometric transforms applied includes:
      AutoContrast, RandomPosterize, RandomSharpness, RandomSolarize
docstr: Args:
      p (float): Probability of applying the transforms
docstr_tok: ["Args",":","p","(","float",")",":","Probability","of","applying","the","transforms"]
code: def __init__(self, p):
      """
      Args:
          p (float): Probability of applying the transforms
      """
      assert isinstance(
          p, float
      ), f"p must be a float value. Found {type(p)}"
      assert p >= 0 and p <= 1
      transforms = [
          RandomPosterizeTransform(),
          RandomSharpnessTransform(),
          RandomSolarizeTransform(),
          AutoContrastTransform(),
      ]
      self.transform = pth_transforms.RandomApply(transforms, p)
      logging.info(
          f"ImgPilRandomPhotometric with prob {p} and {transforms}"
      )
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_random_photometric.py
imports: import logging
  from typing import Any, Dict
  import torchvision.transforms
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from vissl.data.ssl_transforms.pil_photometric_transforms_lib import AutoContrastTransform, RandomPosterizeTransform, RandomSharpnessTransform, RandomSolarizeTransform
label_desc: Use image node_id 1 to create a new ImgPilRandomPhotometric object from inherited base classes: ClassyTransform with example: obj = ImgPilRandomPhotometric(p)